Literature DB >> 19445499

Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins?

Pablo Englebienne1, Nicolas Moitessier.   

Abstract

In our previous report, we investigated the impact of protein flexibility and the presence of water molecules on the pose-prediction accuracy of major docking programs. To complete these investigations, we report herein a study of the impact of these two aspects on the accuracy of scoring functions. To this effect, we developed two sets of protein/ligand complexes made up of ligands cross-docked or cocrystallized with a large variety of proteins, featuring bridging water molecules and demonstrating protein flexibility. Efforts were made to reduce the correlation between the molecular weights of the selected ligands and their binding affinities, a major bias in some previously reported benchmark sets. Using these sets, 18 available scoring functions have been assessed for their accuracy to predict binding affinities and to rank-order compounds by their affinity to cocrystallized proteins. This study confirmed the good and similar accuracy of Xscore, GlideScore, DrugScore(CSD), GoldScore, PLP1, ChemScore, RankScore, and the eHiTS scoring function. Our next investigations demonstrated that most of the assessed scoring functions were much less accurate when the correct protein conformation was not provided. This study also revealed that considering the water molecules for scoring does not greatly affect the accuracy. Finally, this work sheds light on the high correlation between scoring functions and the poor increase in accuracy one can expect from consensus scoring.

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Year:  2009        PMID: 19445499     DOI: 10.1021/ci8004308

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  30 in total

1.  Improving molecular docking through eHiTS' tunable scoring function.

Authors:  Orr Ravitz; Zsolt Zsoldos; Aniko Simon
Journal:  J Comput Aided Mol Des       Date:  2011-11-11       Impact factor: 3.686

2.  Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

Authors:  Oliver Korb; Tim Ten Brink; Fredrick Robin Devadoss Victor Paul Raj; Matthias Keil; Thomas E Exner
Journal:  J Comput Aided Mol Des       Date:  2012-01-10       Impact factor: 3.686

3.  Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction.

Authors:  Traian Sulea; Hervé Hogues; Enrico O Purisima
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

Review 4.  Software for systems biology: from tools to integrated platforms.

Authors:  Samik Ghosh; Yukiko Matsuoka; Yoshiyuki Asai; Kun-Yi Hsin; Hiroaki Kitano
Journal:  Nat Rev Genet       Date:  2011-11-03       Impact factor: 53.242

5.  Improved ligand-protein binding affinity predictions using multiple binding modes.

Authors:  Eva Stjernschantz; Chris Oostenbrink
Journal:  Biophys J       Date:  2010-06-02       Impact factor: 4.033

6.  Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation.

Authors:  James S Wright; James M Anderson; Hooman Shadnia; Tony Durst; John A Katzenellenbogen
Journal:  J Comput Aided Mol Des       Date:  2013-08-24       Impact factor: 3.686

7.  Molecular motions in drug design: the coming age of the metadynamics method.

Authors:  Xevi Biarnés; Salvatore Bongarzone; Attilio Vittorio Vargiu; Paolo Carloni; Paolo Ruggerone
Journal:  J Comput Aided Mol Des       Date:  2011-02-17       Impact factor: 3.686

8.  The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators.

Authors:  Mutasem O Taha; Maha Habash; Mohammad A Khanfar
Journal:  J Comput Aided Mol Des       Date:  2014-03-08       Impact factor: 3.686

9.  Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge.

Authors:  Hervé Hogues; Traian Sulea; Enrico O Purisima
Journal:  J Comput Aided Mol Des       Date:  2014-01-29       Impact factor: 3.686

10.  A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity.

Authors:  Jiesi Luo; Yanzhi Guo; Yun Zhong; Duo Ma; Wenling Li; Menglong Li
Journal:  J Comput Aided Mol Des       Date:  2014-05-01       Impact factor: 3.686

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